A chatterbot or chatbot aims to make a conversation between both human and machine. The machine has been embedded knowledge to identify the sentences and making a decision itself as response to answer a question. The response principle is matching the input sentence from user. From input sentence, it will be scored to get the similarity of sentences, the higher score obtained the more similar of reference sentences. The sentence similarity calculation in this paper using bigram which divides input sentence as two letters of input sentence. The knowledge of chatbot are stored in the database. The chatbot consists of core and interface that is accessing that core in relational database management systems (RDBMS). The database has been employed as knowledge storage and interpreter has been employed as stored programs of function and procedure sets for pattern-matching requirement. The interface is standalone which has been built using programing language of Pascal and Java.

Online Store grows very fast. Online Store helps people to buy the desired product online. Heavy competition among of Online Store provider give a rise to technology development. Many Online Store system not only display the product but also need to be supported by proper products selection to attract the attention of website visitors. As a result, many website visitors are confused when they are going to buy products in Online Store. The number of product variety offered to a customer when he buys goods sometimes more than one product. The problem leads to an idea of developing a products recommendation system. Historical data from visitors and customers can be used to analyze the user needs and products preferences. Association rule using Apriori knowledge will be able to capture the user preference. By identify the user preferences, a valid product recommendation can be developed. This research will analyze the rules in a historical data of purchase from Online Store visitors to get recommendation of products to be displayed. According to experiment result, the ascociation rule is capable to generate precise recommendations with confidence values 76.92%.

Energy consumption in wireless sensor networks (WSN) is an important thing to note. The nature of real-time data transmission on the WSN forces the WSN to have a shorter lifespan. The existibility of WSN certainly requires a strategy that supports it. This paper aims to observe the performance of a wireless sensor network protocol - chimpanzee leader selection optimization (WSN-CLEO). This protocol has been shown to perform better than some hierarchical routing models at sink positions in the WSN field. The clustering of sensor nodes in this WSN consists of two models, namely clustering and classification. Clustering sensor nodes use an algorithm of chimpanzee leader election optimization (CLEO), while classification uses a threshold value of the distance between the sensor nodes and the base station (BS). However, in the grouping of classification, the threshold distance between the sensor nodes and the BS needs to be proven more efficient in WSN energy management. This comparison method uses several distance classification settings on the WSN -CLEO at 60 meters, 80 meters, and 100 meters. After grouping the sensor nodes, the next step is selecting the group leader. The results of the study show insignificant differences in energy efficiency between the first node dead (FND), half node dead (HND), and last node dead (LND) in the comparison of each scenario. However, they have different tendencies, especially in FND.